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Maximilian Selmair


I ❤️ Modelling & Simulation




AnyLogic / Material Flow / AMR Systems

Java / Python / SQLite / IBM ILOG

Maximilia Selmair

I am a seasoned simulation engineer with extensive experience across various industries, specializing in automotive production, logistics, and material flow. With a deep understanding of simulation techniques and their application, I have consistently delivered impactful solutions to enhance operational efficiency and optimize processes. I specialize in leveraging the unparalleled capabilities of AnyLogic software, which stands as the industry's premier choice due to its exceptional flexibility in modeling complex systems and processes.


Throughout my career, I have developed expertise in designing and implementing simulation models that accurately replicate real-world scenarios. By leveraging advanced algorithms and modeling techniques, I have successfully analyzed complex systems, identified bottlenecks, and proposed efficient solutions to streamline operations. One of my key strengths is in building data-driven digital twin models, which offer exceptional flexibility through the adaptive use of imported data. By harnessing this approach, I can create highly adaptable simulations that accurately mirror real-world scenarios, enabling organizations to make informed decisions and optimize their processes.

In addition to my broad simulation knowledge, I possess strong analytical skills that enable me to interpret and leverage data effectively. By extracting meaningful insights from data, I can optimize processes, reduce costs, and improve overall performance. Whether it's analyzing production lines, warehouse layouts, or material flow networks, I can provide valuable recommendations to drive operational excellence.

Collaboration is a key aspect of my approach. I have successfully collaborated with cross-functional teams, including engineers, managers, and stakeholders, to develop simulation models that address specific business needs. By fostering open communication and leveraging the collective expertise of the team, I ensure that the simulation solutions are aligned with organizational goals and yield tangible results.

I am committed to staying abreast of the latest advancements in simulation technologies and industry best practices. This enables me to apply state-of-the-art methodologies and tools to deliver cutting-edge solutions to my clients. I thrive on the challenge of finding innovative ways to optimize processes and continuously improve performance.

A notable aspect of my experience lies in optimizing Autonomous Mobile Robot (AMR) fleets by orchestrating the allocation of tasks to these vehicles. I have been intimately involved in the development of the STR (Smart Transport Robot), a groundbreaking AMR designed by BMW. Through my expertise in AMR fleet management, I have helped streamline operations and enhance efficiency in various contexts.

If you require assistance or consultation in simulation engineering, be it in automotive production, logistics, material flow, or any other industry, I am confident in my ability to provide expert insights and solutions. I look forward to collaborating with you to unlock the full potential of your operations and achieve your desired outcomes.


since 04|2023


Simulation and Modeling Architect (freelance partner)

Discrete Event Simulation / Industrial Engineering /

Production Scheduling / Material Flow Optimization

With ROI EFESO Consulting, I specialize in crafting top-notch, data-infused simulation models tailored for diverse 🌍 industries worldwide. My digital representations 💻📊 serve specific goals, whether it's streamlining material flow, enhancing production scheduling, or determining optimal buffer capacities.

10|2021 to 03|2024


Senior Simulation Engineer

Discrete Event Simulation / Industrial Engineering /

Material Flow Optimization / AGVs & AMRs

As a Simulation Engineer at ⚡️Tesla🔌, I am responsible for overseeing a diverse range of projects that require the resolution of complex problems 👨‍💻 that cannot be effectively addressed through conventional, static calculations 📈. To achieve this, I leverage AnyLogic simulation software 🌌 to analyze systems over extended virtual timeframes, which allows us to incorporate the inherent unpredictability of day-to-day manufacturing processes 🌪🔩⚙️🚀. By conducting simulation studies, we can make informed decisions 💵⏱ with greater confidence 📈📉 before implementing solutions 💵⏱.

09|2017 to 09|2021


PhD Student

Optimizing Vehicle Fleet Operations: Strategies for allocating
immediate Transportation and Charging Tasks in AMR Systems

This thesis 📚 investigates optimisation techniques for scheduling an online autonomous mobile robot system for pickup and delivery with stochastic transportation requests revealed sequentially over a rolling horizon. In contrast to further research, the focus belays in a large-scale specification with a high traffic density, i.e. 100 robots 🤖 in an assembly hall 🔩⚙️🔧 with 50,000 m². The aim of this study 📊📈📉 is to develop interoperable scheduling modules for an optimal overall performance of an AMR system 💨.

10|2014 to 09|2016


Material Flow Simulation

Plant Simulation / SimAssist / SQLite



Streamlining stochastic transportation requests to AMR fleets:
An optimal allocation algorithm for single-transports

currently developed





Advanced Random Number Implementation Techniques: A Guide towards Reliable Scenario Comparisons in Simulation Modelling

Research Gate


Uncertainty, as a certain attendant in our world, is the major reason why simulation modelling takes precedence over static calculations for achieving confident predictions. Random number generators that use computational algorithms can produce values based on random distributions by utilizing an initial seed value to calculate unlimited sequences of random numbers. By using a deterministic method to replicate numbers that appear to be random, it guarantees that stochastic models can be reproduced at any time, which is crucial for simulation models. However, the use of random number streams in modelling and simulation can have some pitfalls which may even mislead experienced developers. This article not only provides an overview of the history of random number generators but also offers practical modelling instructions on how to improve their use in order to produce accurate and dependable scenario comparisons.



Foundational Statistical Methods in Comparative Design for Simulation Experiments

38th European Conference on Modelling and Simulation


This study presents a comprehensive examination of the application of traditional statistical methods to simulation modeling within the hypothetical context of comparing manual and automated production lines in manufacturing. Through a detailed methodology involving the AnyLogic simulation platform and Minitab for statistical analysis, we emphasize the significance of power analysis, two-sample t-tests, and one-way ANOVA in validating and optimizing simulation models. Our hypothetical findings demonstrate the potential of statistical analysis to identify significant efficiency improvements, with a particular focus on the implications of process modifications on automated production lines. The primary contribution of this research lies in illustrating the practical application of statistical tools in simulation studies, serving as a manual for simulation modelers in logistics and manufacturing sectors. By foregrounding the statistical methods over specific operational improvements, this study aims to bridge the gap in literature regarding the integration of foundational statistical analysis within simulation modeling, offering valuable insights for enhancing decision-making and optimization in manufacturing simulations.



Taking randomness for granted:
the complexities of applying random number streams in simulation modelling

36th European Conference on Modelling and Simulation


Uncertainty, as a constant companion of our world, is one major reason why simulation modelling takes precedence over static calculations to achieve accurate predictions. Computational random number generators are able to algorithmically determine values on the basis of random distributions, which utilise seed values to calculate streams of random numbers. This deterministic approach to replicating seemingly non-deterministic numbers ensures stochastic models to be reproducible at any time – one of the major requirements of simulation models. However, there are some pitfalls in the application of random number streams in modelling and simulation, which may even mislead experienced developers. In addition to a general introduction of the history of random number generators, this article shares empirical considerations and means by which the utilisation of random number streams can be improved to deliver valid and reliable results.



Enhancing the efficiency of charging & parking processes for Autonomous Mobile Robot Fleets: A simulative evaluation

Journal of Power Sources


The allocation of tasks to Autonomous Mobile Robots in a production setting in combination with the most efficient parking and charging processes are the focus of this paper. This study presents a simulative evaluation of the theoretical allocation methods developed in Selmair et al. 2020 combined with either hard or dynamic availability rules to ascertain the most efficient parameters of an Autonomous Mobile Robot System. In order to quantify this efficiency, the following Key Performance Indicator (KPI) were considered: number of delayed orders, driven fleet meters and the percentage of available Autonomous Mobile Robot as determined by their state of charge. Additionally, as an alternative energy source, a fast-charging battery developed by Battery Streak Inc. was included in this study. The results show that, in comparison to a conventional and commonly used trivial strategy, our developed strategies provide superior results in terms of the relevant KPI.



The dark side of the database

Guest-Blog for The AnyLogic Modeler


In this post, we want to make you aware of some notable behaviors of the AnyLogic database when working with Excel as the external data source and point out a way to make your data import more reliable.


Optimizing large-scale AMR fleet operations

AnyLogic Conference 2021


Automotive industry leaders use autonomous mobile robots (AMR) in their production facilities to improve productivity. In this case study, Tesla Material Flow Engineer and former BMW Group PhD Student and AMR researcher, Maximilian Selmair, describes standard industry practice when deploying large-scale transporter fleets and demonstrates how AnyLogic cloud-based simulation helps develop optimal task allocation algorithms.


Agent-based simulation model for AMR research

model of the month (Februar 2021)


This cloud-based model utilises the common AnyLogic Transporter to facilitate an entirely flexible task allocation strategy for an AMR fleet. Particularly for large-scale systems, this flexible approach of allocating tasks enhances the performance of the transportation system. Furthermore, a smart Charging & Parking strategy is implemented to improve all necessary sub-processes as well.



Evaluation of algorithm performance for simulated square and non-square logistic assignment problems

35th European Conference on Modelling and Simulation


The optimal allocation of transportation tasks to a fleet of vehicles, especially for large-scale systems of more than 20 Autonomous Mobile Robots (AMRs), remains a major challenge in logistics. Optimal in this context refers to two criteria: how close a result is to the best achievable objective value and the shortest possible computational time. Operations research has provided different methods that can be applied to solve this assignment problem. Our literature review has revealed six commonly applied methods to solve this problem. In this paper, we compared three optimal methods (Integer Linear Programming, Hungarian Method and the Jonker Volgenant Castanon algorithm) to three three heuristic methods (Greedy Search algorithm, Vogel's Approximation Method and Vogel's Approximation Method for non-quadratic Matrices). The latter group generally yield results faster, but were not developed to provide optimal results in terms of the optimal objective value. Every method was applied to 20.000 randomised samples of matrices, which differed in scale and configuration, in simulation experiments in order to determine the results' proximity to the optimal solution as well as their computational time. The simulation results demonstrate that all methods vary in their time needed to solve the assignment problem scenarios as well as in the respective quality of the solution. Based on these results yielded by computing quadratic and non-quadratic matrices of different scales, we have concluded that the Jonker Volgenant Castanon algorithm is deemed to be the best method for solving quadratic and non-quadratic assignment problems with optimal precision. However, if performance in terms of computational time is prioritised for large non-quadratic matrices (50 × 300 and larger), the Vogel's Approximation Method for non-quadratic Matrices generates faster approximated solutions.



Improved decentral task allocation for AGV systems based on Karis Pro

AKWI Journal


In this paper, we extended an existing decentralised method for allocating tasks to AGVs, by additionally considering vehicles which already are assigned to a task. This was achieved by also taking into account the opportunity costs arising from a vehicle passing a current task to another vehicle and subsequently accepting a new task. This loosened restriction is enabling the vehicle fleet for a higher flexibility, which can be used for improving the efficiency of the overall system. By means of simulation, our findings confirm the notion that our extended method – namely Karis Pro+ (KP+) – leads to lower traffic density and higher flexibility, both of which are important KPIs for largescale transport vehicle systems.




Enhancing charging & parking processes of AGV systems:

Progressive theoretical considerations

The Twelfth International Conference on Advances in System Simulation


This paper presents our work in progress for the development of an efficient charging & parking strategy. Our research aim is to develop a strategy that not only provides an efficient approach to charging AGV batteries, but also reduces traffic density in a highly utilised large-scale AGV system. Alongside the current state-of-the-art solution, three new allocation methods are introduced: Trivial+, Pearl Chain and a method based on the Generalised Assignment Problem (GAP). These four methods vary in their scope, in terms of number of vehicles considered, when calculating a decision for a specific vehicle. Furthermore, two types of availability rules for vehicles are introduced and evaluated. Their combination with the allocation methods lay the foundation for future research. All allocation methods and availability rules are explained in detail and this is followed by a summary of the expected outcomes.



Efficient task prioritisation for Autonomous Transport Systems

34rd European Conference on Modelling and Simulation


The efficient distribution of scarce resources has been a challenge in many different fields of research. This paper focuses on the area of operations research, more specifically, Automated Guided Vehicles intended for pickup and delivery tasks. In time delivery in general and flexibility in particular are important KPIs for such systems. In order to meet in time requirements and maximising flexibility, three prioritisation methods embedded in a task allocation system for autonomous transport vehicles are introduced. A case study within the BMW Group aims to evaluate all three methods by means of simulation. The simulation results have revealed differences between the three methods regarding the quality of their solutions as well as their calculation performance. Here, the Flexible Prioritisation Window was found to be superior.



Towards ASP-based scheduling for industrial transport vehicles

Joint Austrian Computer Vision and Robotics Workshop 2020


The increasing number of robots and autonomous vehicles involved in logistics applications leads to new challenges to face for the community of Artificial Intelligence. Web-shop giants, like Amazon or Alibaba for instance, brought this problem to a new level, with huge warehouses and an huge number of orders to deliver with strict deadlines. Coordinating and scheduling such high quantity of tasks over a fleet of autonomous robots is a really complex problem: neither simple imperative greedy algorithms, which compromises over the quality of the solution, nor precise enumeration techniques, which compromises over the solving time, are no more feasible to tackle such problems. In this work, we use Answer Set Programming to tackle real-world logistics problems, involving both dynamic task assignment and planning, at the BMW Group and Incubed IT. Different strategies are tried, and compared to the original imperative approach.




Scheduling charging operations of autonomous AGVs in automotive in-house logistics

Simulation in Production and Logistics 2019


Scheduling approaches for the charging of Automated Guided Vehicles (AGVs) are based on three key components: the timing of charging processes, the selection of a charging station and the duration of the charging process. Based on literature research introduced in this paper, two scheduling approaches have been studied: a rigid approach, based on state-of-the-art solutions, captures the optimal case for a single AGV. A flexible approach, particularly focusing on autonomous behaviour of AGVs, aims for an optimum for the whole AGV fleet. Therefore, the concept of auction-based task allocation is transferred. A closed-loop simulation compares both scheduling approaches for the application of automotive in-house logistics. The flexible approach shows a higher scheduling effectiveness, although influenced by the charging station allocation.



Solving non-quadratic matrices in assignment problems

with an improved Version of Vogel's Approximation Method

33rd European Conference on Modelling and Simulation


The efficient allocation of tasks to vehicles in a fleet of self-driving vehicles (SDV) becomes challenging for largescale systems (e.g. more than hundred vehicles). Operations research provides different methods that can be applied to solve such assignment problems. Integer Linear Programming (ILP), the Hungarian Method (HM) or Vogel’s Approximation Method (VAM) are frequently used in related literature (Paul 2018; Dinagar and Keerthivasan 2018; Nahar et al. 2018; Ahmed et al. 2016; Korukoglu and Ballı 2011; Balakrishnan 1990). The underlying paper proposes an adapted version of VAM which reaches better solutions for non-quadratic matrices, namely Vogel’s Approximation Method for non-quadratic Matrices (VAM-nq). Subsequently, VAM-nq is compared with ILP, HM and VAM by solving matrices of different sizes in computational experiments in order to determine the proximity to the optimal solution and the computation time. The experimental results demonstrated that both VAM and VAM-nq are five to ten times faster in computing results than HM and ILP across all tested matrix sizes. However, we proved that VAM is not able to generate optimal solutions in large quadratic matrices constantly (starting at approx. 15×15) or small non-quadratic matrices (starting at approx. 5×6). In fact, we show that VAM produces insufficient results especially for non-quadratic matrices. The result deviate further from the optimum if the matrix size increases. Our proposed VAM-nq is able to provide similar results as the original VAM for quadratic matrices, but delivers much better results in non-quadratic instances often reaching an optimum solution. This is especially important for practical use cases since quadratic matrices are rather rare.



Exploring opportunities: Optimising production planning

by Factoring in Energy Procurement and Trading Options

Simulation Notes Europe


Motivated by the increasing share of renewable energy in the markets for energy commodities, this study has evaluated the potential for optimising production planning by taking into account disposable options for procuring energy, in this case electricity. For this purpose, a material flow simulation study extended by an electricity price simulation has been executed to examine possible cost scenarios. Our findings support the notion of a potential for further research in new optimisation models involving energy procurement as well as energy trading options.



Job shop scheduling with flexible energy prices

30th European Conference on Modelling and Simulation


The rising energy prices - particularly over the last decade - pose a new challenge for the manufacturing industry. Reactions to climate change, such as the advancement of renewable energies, raise the expectation of further price increases and variations. Regarding the manufacturing industry, production planning and controlling can have a significant influence on the in-plant energy consumption. In this paper, we develop a scheduling method as a linear optimization model with the objective to minimize energy costs in a job shop production system.



Social and ecological capabilities for a sustainable hierarchical production planning

30th European Conference on Modelling and Simulation


Production planning and production control mainly focus on optimising the entire production system of a company. On the basis of hierarchical planning as a suitable method for solving this task, this paper shows - besides the economic dimension taken into account so far - that there are also social and ecological effects which will have to be considered in the process of plan-ning. For this purpose, we would like to indicate here which social and ecological parameters can be or have already been taken into account for master production scheduling, for lot sizing and resource scheduling. As a result, an overview has been created which presents the existing concepts of sustainable production planning and production control as well as the existing deficits regarding the sustainability perspective.



Potential of reducing the total energy consumption

by Scheduling a Jobshop Production System

Simulation in Production and Logistics 2015


This research paper has evaluated the potential of reducing energy consumption by scheduling jobshop production systems with machines using standby modes in free times. The initial introduction of the planning issue is followed by a description of the approach to assess the available potential. Subsequently, the research procedure by means of simulation, a detailed discussion of the results and a perspective on future research is given. Moreover, the notion that scheduling influences the energy consumption in jobshop production systems is supported. The presented simulation research also documents that there is no direct correlation between energy consumption and total lead time, although this was presumed usually. Finally, this paper provides a forecast for a possible optimisation model as well as an exemplary model with an energy-optimised schedule plan.



Automatisation of the SAP GUI via scripting

Ostbayerische Technische Hochschule Regensburg


The core product provided by the SAP AG is the software system called “Enterprise Resource Planning” or short ERP which can be used to support all business process in a company. This includes accounting, human resources and logistics as well as different industry-specific solutions. In order to ensure the functionality of this complex software system, it is imperative to handle the corresponding master data conscientiously and manage them continuously. The resulting work processes are mostly recurring and require a substantial amount of time in their execution. Resolving this issue was the subject of my master’s thesis at Siemens AG. The solution I provided by using SAP GUI Scripting increased the efficiency of several operating procedures and is a intensively utilised tool in different departments of Siemens AG in its Regensburg location. No literature except the API had existed before I started working on this topic. In the course of the thesis, a substantial amount of research has been carried out regarding the entire topic in order to analyse the functions and difficulties of this emergent SAP interface, and in order to be able to handle it properly.

09|2017 bis 05|2021

Doctoral student

Optimising Operations for Fleets of Autonomous Mobile Robots: Strategies for allocating immediate Transportation and Charging Tasks in large-scale Systems

07|2014 bis 09|2017

Doctoral student

nicht abgeschlossene Promotion im Bereich Sustainable Production Planning

03|2013 bis 07|2014


Master of Arts


Automatisierung in der SAP GUI per Scripting


Prof. Dr. Frank Herrmann




10|2009 bis 02|2013

Business Information Technology

Bachelor of Science


Planung und Realisierung einer Unternehmensneugründung


Prof. Dr. Alexander Söder



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